Target Discovery and Validation Reviews and Protocols

52 Imoto et al.
Table 3
List of Root Genes of Identified Drug-Active Pathways (13)
Gene name GO annotations
CHA1 Serine family amino acid catabolism, mitochondrial nucleoid
CAF16 Regulation of transcription, cytoplasm
PNC1 Chromatin silencing, cytoplasm
ADE5,7 Purine base metabolism, cytoplasm
PIL1 Response to heat, cytoplasm
KGD1 Tricarboxylic acid cycle, mitochondrial matrix
FRE1 Iron ion transport, plasma membrane
ADE13 Purine base metabolism
IZH2 Lipid metabolism, integral to membrane
GO, gene ontology.
relations correctly, when the data have sufficient accuracy and information. However,
this method requires discretization of the expression values into two levels (0 or 1),
and the quantization probably causes information loss. By choosing the threshold η
appropriately in a heuristic manner, it is reported that this naive algorithm works well
in practice, although the method is not theoretically well founded.
3. In practice, biological experiments that disrupt the target druggable genes are
needed for confirming the results of the analysis.
4. For identifying drug-affected genes by the virtual gene technique, we simply consider
genes that have five or more virtual genes as the parent genes as the putative
drug-affected genes. That is, genes are under direct influence of the virtual genes.
However, a gene that has only one virtual gene as its parent may be the primary
drug-affected gene, depending on the mode of action for a given drug, and this
possibility must be analyzed case by case.
5. In Bayesian network literature, for example, Neapolitan (25), it is shown that
determining the optimal network is an NP-hard problem. However, recent development
of supercomputer technology enables us to optimize the small gene network,
including 40 or fewer genes, by using a suitable algorithm (26). However, for
larger numbers of genes, we use a heuristic strategy such as a greedy hill-climbing
algorithm to discipher graph structure.
6. The simplest way for discretizing microarray gene expression data is the use of
some thresholds: let {u 1 ,…,u m } be a finite set of discrete values and {I 1 ,…, I m }
be regions satisfying j I j = R and I i I j =∅for i ≠ j. An expression value x ij is
then transformed to u j when x ij ∈ I j (see Friedman et al. [6]). An alternative way
for discretization is the use of k-means clustering (8). Also, Shmulevich and Zhang
(27) proposed a method for binalizing gene expression data.
7. The nonparametric regression model in the Bayesian networks of Imoto et al. (9,10)
is an additive regression model. However, in reality, there are several known
transcriptional relationships that are more additive. Imoto et al. (28) proposed a